Authors: Manav Kumar; Sharifuddin Mondal
Addresses: Department of Mechanical Engineering, National Institute of Technology Patna, Bihar, 800005, India ' Department of Mechanical Engineering, National Institute of Technology Patna, Bihar, 800005, India
Abstract: In this work, well known existing estimation algorithms like extended Kalman filter (EKF) and unscented Kalman filter (UKF) with different adaptive extensions are implemented on target tracking problem for passive tracking. To deal with model uncertainty and uncertain noises, the process and measurement noise covariances are adapted based on innovation and residual sequences. Different adaptation rules for adjusting the noise covariance are examined. For the robustness performance analysis of each algorithm, target loss that occurred at last time of simulation is accounted for with consideration of a 2% estimation error. The effectiveness of filter performance is evaluated on the basis of root mean square error, average target loss and relative computational time with Monte Carlo simulation. Simulation results demonstrate that adaptive version of traditional filters have improved tracking performance with a significant computational burden in terms of estimation accuracy and track loss.
Keywords: extended Kalman filter; EKF; unscented Kalman filter; UKF; KF-UKF; adaptive estimation; passive tracking; target loss.
International Journal of Space Science and Engineering, 2023 Vol.6 No.4, pp.350 - 371
Received: 02 May 2022
Accepted: 14 Mar 2023
Published online: 22 May 2023 *